






'''simple docstring'''







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DetrImageProcessor




class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__=7   ,	UpperCamelCase__=3   ,	UpperCamelCase__=30   ,	UpperCamelCase__=400   ,	UpperCamelCase__=True   ,	UpperCamelCase__=None   ,	UpperCamelCase__=True   ,	UpperCamelCase__=1 / 255   ,	UpperCamelCase__=True   ,	UpperCamelCase__=[0.5, 0.5, 0.5]   ,	UpperCamelCase__=[0.5, 0.5, 0.5]   ,	UpperCamelCase__=True   ,	):






         """simple docstring"""


         a_						     =			size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
         a_						     =			parent
         a_						     =			batch_size
         a_						     =			num_channels
         a_						     =			min_resolution
         a_						     =			max_resolution
         a_						     =			do_resize
         a_						     =			size
         a_						     =			do_rescale
         a_						     =			rescale_factor
         a_						     =			do_normalize
         a_						     =			image_mean
         a_						     =			image_std
         a_						     =			do_pad







  def    _a       (			self	):






         """simple docstring"""


         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_pad": self.do_pad,
         }







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=False	):






         """simple docstring"""


         if not batched:
                a_						     =			image_inputs[0]
                if isinstance(UpperCamelCase__   ,	Image.Image	):
                       a_     ,	a_						     =			image.size
                else:
                       a_     ,	a_						     =			image.shape[1], image.shape[2]
                if w < h:
                       a_						     =			int(self.size['shortest_edge'] * h / w	)
                       a_						     =			self.size['shortest_edge']
                elif w > h:
                       a_						     =			self.size['shortest_edge']
                       a_						     =			int(self.size['shortest_edge'] * w / h	)
                else:
                       a_						     =			self.size['shortest_edge']
                       a_						     =			self.size['shortest_edge']

         else:
                a_						     =			[]
                for image in image_inputs:
                       a_     ,	a_						     =			self.get_expected_values([image]	)
                       expected_values.append((expected_height, expected_width)	)
                a_						     =			max(UpperCamelCase__   ,	key=lambda UpperCamelCase__	: item[0]	)[0]
                a_						     =			max(UpperCamelCase__   ,	key=lambda UpperCamelCase__	: item[1]	)[1]

         return expected_height, expected_width





@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE       (__A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Any      			=     DetrImageProcessor if is_vision_available() else None







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			DetrImageProcessingTester(self	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         return self.image_processor_tester.prepare_image_processor_dict()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(UpperCamelCase__   ,	'image_mean'	)	)
         self.assertTrue(hasattr(UpperCamelCase__   ,	'image_std'	)	)
         self.assertTrue(hasattr(UpperCamelCase__   ,	'do_normalize'	)	)
         self.assertTrue(hasattr(UpperCamelCase__   ,	'do_rescale'	)	)
         self.assertTrue(hasattr(UpperCamelCase__   ,	'rescale_factor'	)	)
         self.assertTrue(hasattr(UpperCamelCase__   ,	'do_resize'	)	)
         self.assertTrue(hasattr(UpperCamelCase__   ,	'size'	)	)
         self.assertTrue(hasattr(UpperCamelCase__   ,	'do_pad'	)	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.image_processing_class.from_dict(self.image_processor_dict	)
         self.assertEqual(image_processor.size   ,	{'shortest_edge': 18, 'longest_edge': 1_333}	)
         self.assertEqual(image_processor.do_pad   ,	UpperCamelCase__	)

         a_						     =			self.image_processing_class.from_dict(
             self.image_processor_dict   ,	size=42   ,	max_size=84   ,	pad_and_return_pixel_mask=UpperCamelCase__	)
         self.assertEqual(image_processor.size   ,	{'shortest_edge': 42, 'longest_edge': 84}	)
         self.assertEqual(image_processor.do_pad   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         pass







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         a_						     =			prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=UpperCamelCase__	)
         for image in image_inputs:
                self.assertIsInstance(UpperCamelCase__   ,	Image.Image	)

         # Test not batched input
         a_						     =			image_processing(image_inputs[0]   ,	return_tensors='pt'	).pixel_values

         a_     ,	a_						     =			self.image_processor_tester.get_expected_values(UpperCamelCase__	)

         self.assertEqual(
             encoded_images.shape   ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,	)

         # Test batched
         a_     ,	a_						     =			self.image_processor_tester.get_expected_values(UpperCamelCase__   ,	batched=UpperCamelCase__	)

         a_						     =			image_processing(UpperCamelCase__   ,	return_tensors='pt'	).pixel_values
         self.assertEqual(
             encoded_images.shape   ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.image_processing_class(**self.image_processor_dict	)
         # create random numpy tensors
         a_						     =			prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=UpperCamelCase__   ,	numpify=UpperCamelCase__	)
         for image in image_inputs:
                self.assertIsInstance(UpperCamelCase__   ,	np.ndarray	)

         # Test not batched input
         a_						     =			image_processing(image_inputs[0]   ,	return_tensors='pt'	).pixel_values

         a_     ,	a_						     =			self.image_processor_tester.get_expected_values(UpperCamelCase__	)

         self.assertEqual(
             encoded_images.shape   ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,	)

         # Test batched
         a_						     =			image_processing(UpperCamelCase__   ,	return_tensors='pt'	).pixel_values

         a_     ,	a_						     =			self.image_processor_tester.get_expected_values(UpperCamelCase__   ,	batched=UpperCamelCase__	)

         self.assertEqual(
             encoded_images.shape   ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.image_processing_class(**self.image_processor_dict	)
         # create random PyTorch tensors
         a_						     =			prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=UpperCamelCase__   ,	torchify=UpperCamelCase__	)
         for image in image_inputs:
                self.assertIsInstance(UpperCamelCase__   ,	torch.Tensor	)

         # Test not batched input
         a_						     =			image_processing(image_inputs[0]   ,	return_tensors='pt'	).pixel_values

         a_     ,	a_						     =			self.image_processor_tester.get_expected_values(UpperCamelCase__	)

         self.assertEqual(
             encoded_images.shape   ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,	)

         # Test batched
         a_						     =			image_processing(UpperCamelCase__   ,	return_tensors='pt'	).pixel_values

         a_     ,	a_						     =			self.image_processor_tester.get_expected_values(UpperCamelCase__   ,	batched=UpperCamelCase__	)

         self.assertEqual(
             encoded_images.shape   ,	(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )   ,	)







  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,	'r'	) as f:
                a_						     =			json.loads(f.read()	)

         a_						     =			{'image_id': 39_769, 'annotations': target}

         # encode them
         a_						     =			DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'	)
         a_						     =			image_processing(images=UpperCamelCase__   ,	annotations=UpperCamelCase__   ,	return_tensors='pt'	)

         # verify pixel values
         a_						     =			torch.Size([1, 3, 800, 1_066]	)
         self.assertEqual(encoding['pixel_values'].shape   ,	UpperCamelCase__	)

         a_						     =			torch.tensor([0.2_796, 0.3_138, 0.3_481]	)
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,	UpperCamelCase__   ,	atol=1e-4	)	)

         # verify area
         a_						     =			torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,	UpperCamelCase__	)	)
         # verify boxes
         a_						     =			torch.Size([6, 4]	)
         self.assertEqual(encoding['labels'][0]['boxes'].shape   ,	UpperCamelCase__	)
         a_						     =			torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,	UpperCamelCase__   ,	atol=1e-3	)	)
         # verify image_id
         a_						     =			torch.tensor([39_769]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,	UpperCamelCase__	)	)
         # verify is_crowd
         a_						     =			torch.tensor([0, 0, 0, 0, 0, 0]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,	UpperCamelCase__	)	)
         # verify class_labels
         a_						     =			torch.tensor([75, 75, 63, 65, 17, 17]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,	UpperCamelCase__	)	)
         # verify orig_size
         a_						     =			torch.tensor([480, 640]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,	UpperCamelCase__	)	)
         # verify size
         a_						     =			torch.tensor([800, 1_066]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,	UpperCamelCase__	)	)







  @slow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'   ,	'r'	) as f:
                a_						     =			json.loads(f.read()	)

         a_						     =			{'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

         a_						     =			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'	)

         # encode them
         a_						     =			DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'	)
         a_						     =			image_processing(images=UpperCamelCase__   ,	annotations=UpperCamelCase__   ,	masks_path=UpperCamelCase__   ,	return_tensors='pt'	)

         # verify pixel values
         a_						     =			torch.Size([1, 3, 800, 1_066]	)
         self.assertEqual(encoding['pixel_values'].shape   ,	UpperCamelCase__	)

         a_						     =			torch.tensor([0.2_796, 0.3_138, 0.3_481]	)
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,	UpperCamelCase__   ,	atol=1e-4	)	)

         # verify area
         a_						     =			torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,	UpperCamelCase__	)	)
         # verify boxes
         a_						     =			torch.Size([6, 4]	)
         self.assertEqual(encoding['labels'][0]['boxes'].shape   ,	UpperCamelCase__	)
         a_						     =			torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,	UpperCamelCase__   ,	atol=1e-3	)	)
         # verify image_id
         a_						     =			torch.tensor([39_769]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,	UpperCamelCase__	)	)
         # verify is_crowd
         a_						     =			torch.tensor([0, 0, 0, 0, 0, 0]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,	UpperCamelCase__	)	)
         # verify class_labels
         a_						     =			torch.tensor([17, 17, 63, 75, 75, 93]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,	UpperCamelCase__	)	)
         # verify masks
         a_						     =			822_873
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,	UpperCamelCase__	)
         # verify orig_size
         a_						     =			torch.tensor([480, 640]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,	UpperCamelCase__	)	)
         # verify size
         a_						     =			torch.tensor([800, 1_066]	)
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,	UpperCamelCase__	)	)












'''simple docstring'''







def       __UpperCamelCase					(      lowercase_       :  int = 1_000_000   ):
       """simple docstring"""




       a_						     =			set(range(3   ,       lowercase_   ,       2   )   )
       primes.add(2   )
       for p in range(3   ,       lowercase_   ,       2   ):
              if p not in primes:
                     continue
              primes.difference_update(set(range(p * p   ,       lowercase_   ,       lowercase_   )   )   )

       a_						     =			[float(lowercase_   ) for n in range(limit + 1   )]

       for p in primes:
              for n in range(lowercase_   ,       limit + 1   ,       lowercase_   ):
                     phi[n] *= 1 - 1 / p

       return int(sum(phi[2:]   )   )


if __name__ == "__main__":
  print(f"""{solution() = }""")




